An Ounce of Prevention for Multiple Myeloma Requires Data, Data, Data

Dr. Rafat Abonour has been studying multiple myeloma for nearly 20 years. He has built a myeloma clinic at Indiana University School of Medicine, where he spends the majority of his time seeing patients and conducting clinical research trials. But to identify risk factors or even a cure for multiple myeloma, his research group needs to be able to integrate and analyze various types of patient data on a large scale.

“I know exactly what these patients go through,” Rafat said. Multiple myeloma, a rare type of blood cancer, is estimated to cause 12,770 deaths in the U.S. in 2018, accounting for 2% of all cancer deaths according to the National Cancer Institute. Only half of the people with this disease, on average, survive for five years or more following diagnosis. Myeloma cells collect in the bone marrow where they can damage the solid part of the bone.

Multiple myeloma: X-ray with multiple osteolytic lesions in the forearm. Credit: Hellerhoff.

Like all physicians, Rafat relies on his expertise and experience to look for patterns that inform how he treats new patients who walk into his clinic. But this is no longer enough — today Rafat is looking for more robust ways of mining patient data to inform more personalized treatments.

Dr. Rafat Abonour, IU School of Medicine.

“We go to an expert when we are sick because they’ve seen a lot of patients like us. They recognize patterns and they develop treatment approaches over the years. But in the absence of expertise, what do we do?” Rafat said. “I have a huge amount of respect for community oncologists who treat many different types of cancer, from breast cancer to lung cancer, colon cancer and lymphoma. I admire these oncologists’ ability to manage so many different patients. How can you possibly acquire the expertise to treat all of these different diseases effectively? It’s mind-boggling to me. If we could use big data to develop algorithms based on patients’ individual disease characteristics, we could better classify and treat these patients.”

Multiple myeloma is preceded by a pre-myeloma condition called MGUS, monoclonal gammopathy of unknown significance. MGUS is characterized by blood cells that carry genetic changes and accumulation of an abnormal protein known as monoclonal protein or M protein.

“MGUS occurs when abnormal plasma cells produce M protein. The protein isn’t harmful for most people. But if too much M protein accumulates, it crowds out healthy cells in your bone marrow and can damage other tissues in your body. The precise cause of accumulating M protein isn’t known. Genetic changes and environmental triggers appear to play a role.” — Mayo Clinic

“There’s a lot of speculation about what drives MGUS to become myeloma,” Rafat said. “My lab has an interest in both preventing multiple myeloma and curing it.”

In terms of prevention, Rafat’s lab group is working to identify the factors that drive a patient with MGUS to develop myeloma. But for this, they need patient data — lots of it. Rafat is currently organizing a clinical trial involving weight reduction surgery for obese patients with pre-myeloma.

“There’s some interest in obesity as a risk factor for myeloma,” Rafat said. To further study obesity as a risk factor, Rafat and his lab group at the IU School of Medicine plan to study patients who are undergoing weight reduction surgery, to find out whether weight loss can reverse the pre-myeloma condition MGUS. If they find that weight loss can in fact reduce the risk of developing multiple myeloma, they plan to use tissue samples from patients who have undergone weight reduction surgery for genomic analysis to explore potential genetic drivers linking obesity with the disease, as targets for interventions. The research team will be using LifeOmic’s Precision Health Cloud platform to integrate and analyze patient data such as health and behavioral intervention outcomes over time, clinical and genomic data. Having all of this information for many different patients in one place will hopefully allow Rafat and his colleagues to better identify risk factors for multiple myeloma and potential genetic markers related to these risk factors.

A screenshot of LifeOmic’s Precision Health Cloud Insights viewer.

Rafat’s focus on prevention and identification of risk factors for multiple myeloma is all the more important because no cure exists for the disease today. Treatment currently can put patients into remission, but patients often relapse. It’s also unclear why some patients relapse more quickly than others. While genomic sequencing for individual patients has the power to inform treatment upfront, this has yet to become a standard practice in the treatment of multiple myeloma. Patients typically only receive genetic testing upon relapse.

“The question we still have is, why does one patient spend years in remission, while another relapses quickly following treatment?” Rafat says. “In my lab, we are also trying to understand how to better classify myeloma. One way we are trying to do this is through genomic sequencing of bone marrow to look at myeloma cells and the neighborhoods in which they live, or their microenvironments, and through single cell DNA sequencing to look at the heterogeneity of the cancer cells in each patient. Using this information, we can learn how to better manage different patients based on how likely they will be to quickly relapse, or which drugs their cancer cells will be most sensitive to.”

Being able to better classify and treat patients based on the unique genetic characteristics of their own myeloma cells requires data, and not just data from the patients Rafat sees in his own clinic, but data from patients across many clinics and research groups, he says. But the problem of wrangling this data, and making disparate datasets play nice for searching and querying, remains largely unsolved.

“Having LifeOmic help us put these data together in a single platform, so that we can in a meaningful way mine these data, will be an important part of our research process,” Rafat said. “The more data we have, the more we are going to learn how to truly overcome this disease. You can’t tell from a hundred patients what the underlying trends are.”

Two years ago, Rafat and his research group were able to devise a staging system for multiple myeloma by looking at clinical trial outcome data for over 17,000 patients. Based on this system, physicians today are better able to inform patients of median survival rates given their stage at diagnosis.

“By getting more real-world data from multiple myeloma patients, and mining that data, we are able to better understand how different patients should be diagnosed and treated,” Rafat said. “If we can add layers of genetic information onto this data, we will be able to dig deeper into prognosis, drug responsiveness and survival trends based on individual characteristics. I think we’ve only touched the tip of the iceberg in terms of what we can do for patients with multiple myeloma if we can combine and analyze their clinical and genomic data on a broad scale.”

Rafat and his research collaborators are also exploring how genomic sequencing can inform which drugs are given to which patients, based on potentially debilitating drug side effects.

“We also need to find better markers for drug toxicity for these patients,” Rafat said. “Some patients will have a horrible reaction to a particular drug, while others do fine with that same drug. One question we might ask is whether this is due to the individual’s genetic makeup. Part of our research involves identifying biomarkers for drug toxicity. We are building a cohort of patients that can help us systematically identify which drugs work best for which patients. This is very important, because once you develop a side effect from a drug, it can be very difficult to get rid of, especially for impacts like nerve damage. Once a patient develops neuropathy, it’s impossible to reverse. The best option is to prevent such side effects in the first place.”

We asked Dr. Abonour to tell us more about how genomic sequencing and precision health technologies are changing his approach to diagnosing, treating and potentially helping to prevent multiple myeloma among the patients he sees.

This micrograph shows abundant (malignant) plasma cells. Credit: Nephron.

LifeOmic: Has genomic data made an impact yet in treatment or prevention of multiple myeloma?

Dr. Rafat Abonour: A major focus area of genomic studies to date has been gene expression profiling. Gene expression profiling helps us better understand the biology of multiple myeloma and has allowed classification of multiple myeloma into different subgroups. Each subgroup behaves differently, and knowing this information for an individual patient can help guide treatment decisions. Gene expression profiling has also been used to confirm novel drug targets for multiple myeloma and will be helpful as we continue to develop new therapies.

We are also planning research to investigate how patients develop symptomatic myeloma from the precursor disease called MGUS (monoclonal gammopathy of undetermined significance). Targeting the factors that lead to the development of myeloma may help us prevent myeloma.

LifeOmic: How is genetic information being used to treat individual patients today?

Dr. Rafat Abonour: A key use of a patient’s genetic information today is risk stratification. Based on a patient’s cytogenetics, or chromosomal abnormalities, that patient is identified as being at standard risk, intermediate risk or high risk, with high-risk patients having more aggressive, more difficult to treat disease. The physician uses this information to determine the best course of therapy for an individual patient. In addition, there are early efforts ongoing to identify actionable mutations in relapse patients. Targeting these will allow us to search for the appropriate drug for the patients’ mutation(s).

LifeOmic: What should patients know about the possibility of genetic testing and how it may inform treatment of multiple myeloma?

Dr. Rafat Abonour: One of the problems we face when treating multiple myeloma is the heterogeneity of the disease burden at diagnosis and the fact that the composition of the myeloma cells can change as a patient receives therapy, allowing the myeloma cells to become resistant to the therapy. The ability to combine a wide range of patient genetic data with corresponding clinical data (treatment response data and side effect data) will allow us to better classify myeloma patients and provide treatments that maximize response and minimize side effects.

LifeOmic: What do you think are the most pressing issues in terms of understanding this disease, and how can big data and cloud technologies help?

Dr. Rafat Abonour: The cloud allows us to view data from multiple databases in one place, from across multiple patients. We now have the ability to look at similarities or differences with respect to clinical outcomes, genetic profiles, socioeconomic data and environmental data across thousands of patients. With this technology, we will also be in a position to form collaborations at Indiana University and beyond, to continue to expand the myeloma cohort we are building. Doing this will allow us to better understand risk factors for multiple myeloma, optimize treatment for individual patients and identify new therapeutic targets that could lead to the development of new therapies for multiple myeloma.

LifeOmic: What does precision medicine mean to you, in your field?

Dr. Rafat Abonour: My goal is to understand how to prevent and cure multiple myeloma. At the highest level, precision medicine means getting the right drug to the right patient at the right time. If we can do this for each patient, we will improve outcomes for individual patients and may achieve the goal of a cure.

LifeOmic: How are immunotherapies and metabolic interventions being leveraged or explored for treatment of this disease?

Dr. Rafat Abonour: Immunotherapies are likely to be a key part of the puzzle when it comes to preventing relapse and improving overall outcomes for patients with multiple myeloma. Two areas of interest are monoclonal antibodies and Adoptive T-Cell therapy.

Monoclonal antibodies target different proteins on the surface of myeloma cells, leading to cell death. There have been two new monoclonal antibodies approved in the last few years for the treatment of multiple myeloma, and there are additional monoclonal antibodies being studied for potential use.

Adoptive T-cell therapy utilizes the patient’s own T-cells (lymphocytes, a type of immune or white blood cell). T-cells are removed from the patient and activated with an antigen receptor that is specific to an antigen on the myeloma cell (imagine a lock and key type of specificity). The activated T-cells are then reintroduced to the patient. The goal of the therapy is for the activated T-cells to reproduce and to begin to attack the cancer cells. Although there are no Adoptive T-cell therapies approved for the treatment of multiple myeloma, there are several in development. One of our goals is to explore patient-specific targets for immunotherapy. This will increase the specificities of the genetically manipulated T-cells. We would also like to understand the reason for the immune paralysis in our patients that allows the myeloma cells to survive (by evading the body’s own immune response). Identifying the genetic reasons for immune paralysis will potentially lead to an elimination of this problem.


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